Biostat 203B Homework 2

Due Feb 7, 2025 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.7.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.2    fastmap_1.2.0     cli_3.6.3        
 [5] tools_4.4.2       htmltools_0.5.8.1 rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_1.8.9    xfun_0.50        
[13] digest_0.6.37     rlang_1.1.4       evaluate_1.0.1   

Load necessary libraries (you can add more as needed).

library(arrow)

Attaching package: 'arrow'

The following object is masked from 'package:utils':

    timestamp

library(data.table)
library(duckdb)

Loading required package: DBI

library(memuse)
library(pryr)

Attaching package: 'pryr'

The following object is masked from 'package:data.table':

    address

library(R.utils)

Loading required package: R.oo

Loading required package: R.methodsS3

R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.

R.oo v1.27.0 (2024-11-01 18:00:02 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

The following object is masked from 'package:R.methodsS3':

    throw

The following objects are masked from 'package:methods':

    getClasses, getMethods

The following objects are masked from 'package:base':

    attach, detach, load, save

R.utils v2.12.3 (2023-11-18 01:00:02 UTC) successfully loaded. See ?R.utils for help.

Attaching package: 'R.utils'

The following object is masked from 'package:arrow':

    timestamp

The following object is masked from 'package:utils':

    timestamp

The following objects are masked from 'package:base':

    cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()      masks data.table::between()
✖ purrr::compose()      masks pryr::compose()
✖ lubridate::duration() masks arrow::duration()
✖ tidyr::extract()      masks R.utils::extract()
✖ dplyr::filter()       masks stats::filter()
✖ dplyr::first()        masks data.table::first()
✖ lubridate::hour()     masks data.table::hour()
✖ lubridate::isoweek()  masks data.table::isoweek()
✖ dplyr::lag()          masks stats::lag()
✖ dplyr::last()         masks data.table::last()
✖ lubridate::mday()     masks data.table::mday()
✖ lubridate::minute()   masks data.table::minute()
✖ lubridate::month()    masks data.table::month()
✖ purrr::partial()      masks pryr::partial()
✖ lubridate::quarter()  masks data.table::quarter()
✖ lubridate::second()   masks data.table::second()
✖ purrr::transpose()    masks data.table::transpose()
✖ lubridate::wday()     masks data.table::wday()
✖ lubridate::week()     masks data.table::week()
✖ dplyr::where()        masks pryr::where()
✖ lubridate::yday()     masks data.table::yday()
✖ lubridate::year()     masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Display memory information of your computer

memuse::Sys.meminfo()

Totalram:  16.000 GiB 
Freeram:    4.654 GiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/

total 12306248
-rw-r--r--@ 1 yanzisun  staff    19928140 Jun 24  2024 admissions.csv.gz
-rw-r--r--@ 1 yanzisun  staff      427554 Apr 12  2024 d_hcpcs.csv.gz
-rw-r--r--@ 1 yanzisun  staff      876360 Apr 12  2024 d_icd_diagnoses.csv.gz
-rw-r--r--@ 1 yanzisun  staff      589186 Apr 12  2024 d_icd_procedures.csv.gz
-rw-r--r--@ 1 yanzisun  staff       13169 Oct  3 09:07 d_labitems.csv.gz
-rw-r--r--@ 1 yanzisun  staff    33564802 Oct  3 09:07 diagnoses_icd.csv.gz
-rw-r--r--@ 1 yanzisun  staff     9743908 Oct  3 09:07 drgcodes.csv.gz
-rw-r--r--@ 1 yanzisun  staff   811305629 Apr 12  2024 emar.csv.gz
-rw-r--r--@ 1 yanzisun  staff   748158322 Apr 12  2024 emar_detail.csv.gz
-rw-r--r--@ 1 yanzisun  staff     2162335 Apr 12  2024 hcpcsevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff  2592909134 Oct  3 09:08 labevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff   117644075 Oct  3 09:08 microbiologyevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff    44069351 Oct  3 09:08 omr.csv.gz
-rw-r--r--@ 1 yanzisun  staff     2835586 Apr 12  2024 patients.csv.gz
-rw-r--r--@ 1 yanzisun  staff   525708076 Apr 12  2024 pharmacy.csv.gz
-rw-r--r--@ 1 yanzisun  staff   666594177 Apr 12  2024 poe.csv.gz
-rw-r--r--@ 1 yanzisun  staff    55267894 Apr 12  2024 poe_detail.csv.gz
-rw-r--r--@ 1 yanzisun  staff   606298611 Apr 12  2024 prescriptions.csv.gz
-rw-r--r--@ 1 yanzisun  staff     7777324 Apr 12  2024 procedures_icd.csv.gz
-rw-r--r--@ 1 yanzisun  staff      127330 Apr 12  2024 provider.csv.gz
-rw-r--r--@ 1 yanzisun  staff     8569241 Apr 12  2024 services.csv.gz
-rw-r--r--@ 1 yanzisun  staff    46185771 Oct  3 09:08 transfers.csv.gz

ls -l ~/mimic/icu/

total 8506784
-rw-r--r--@ 1 yanzisun  staff       41566 Apr 12  2024 caregiver.csv.gz
-rw-r--r--@ 1 yanzisun  staff  3502392765 Apr 12  2024 chartevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff       58741 Apr 12  2024 d_items.csv.gz
-rw-r--r--@ 1 yanzisun  staff    63481196 Apr 12  2024 datetimeevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff     3342355 Oct  3 07:36 icustays.csv.gz
-rw-r--r--@ 1 yanzisun  staff   311642048 Apr 12  2024 ingredientevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff   401088206 Apr 12  2024 inputevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff    49307639 Apr 12  2024 outputevents.csv.gz
-rw-r--r--@ 1 yanzisun  staff    24096834 Apr 12  2024 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage; all these readers can take gz file as input without explicit decompression.)

Solution: The fread function is the fastest.

I used str() to check the parsed data types and found: read.csv in base R creates a data.frame; read_csv in tidyverse creates a tibble, and fread in the data.table package creates a data.table which also behaves like data.frame.

The memory use for each function is shown below.

system.time(base_read <- read.csv("~/mimic/hosp/admissions.csv.gz"))

   user  system elapsed 
  6.793   0.072   6.907 

system.time(tidyverse_read <- read_csv("~/mimic/hosp/admissions.csv.gz"))

Rows: 546028 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl  (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

   user  system elapsed 
  1.301   0.113   0.797 

system.time(fread_read <- fread("~/mimic/hosp/admissions.csv.gz"))

   user  system elapsed 
  0.720   0.115   0.426 

object_size(base_read)

200.10 MB

object_size(tidyverse_read)

70.02 MB

object_size(fread_read)

63.47 MB

str(base_read)

'data.frame':   546028 obs. of  16 variables:
 $ subject_id          : int  10000032 10000032 10000032 10000032 10000068 10000084 10000084 10000108 10000117 10000117 ...
 $ hadm_id             : int  22595853 22841357 25742920 29079034 25022803 23052089 29888819 27250926 22927623 27988844 ...
 $ admittime           : chr  "2180-05-06 22:23:00" "2180-06-26 18:27:00" "2180-08-05 23:44:00" "2180-07-23 12:35:00" ...
 $ dischtime           : chr  "2180-05-07 17:15:00" "2180-06-27 18:49:00" "2180-08-07 17:50:00" "2180-07-25 17:55:00" ...
 $ deathtime           : chr  "" "" "" "" ...
 $ admission_type      : chr  "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr  "P49AFC" "P784FA" "P19UTS" "P06OTX" ...
 $ admission_location  : chr  "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr  "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr  "Medicaid" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr  "English" "English" "English" "English" ...
 $ marital_status      : chr  "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr  "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : chr  "2180-05-06 19:17:00" "2180-06-26 15:54:00" "2180-08-05 20:58:00" "2180-07-23 05:54:00" ...
 $ edouttime           : chr  "2180-05-06 23:30:00" "2180-06-26 21:31:00" "2180-08-06 01:44:00" "2180-07-23 14:00:00" ...
 $ hospital_expire_flag: int  0 0 0 0 0 0 0 0 0 0 ...

str(tidyverse_read)  

spc_tbl_ [546,028 × 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ subject_id          : num [1:546028] 1e+07 1e+07 1e+07 1e+07 1e+07 ...
 $ hadm_id             : num [1:546028] 22595853 22841357 25742920 29079034 25022803 ...
 $ admittime           : POSIXct[1:546028], format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct[1:546028], format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct[1:546028], format: NA NA ...
 $ admission_type      : chr [1:546028] "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr [1:546028] "P49AFC" "P784FA" "P19UTS" "P06OTX" ...
 $ admission_location  : chr [1:546028] "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr [1:546028] "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr [1:546028] "Medicaid" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr [1:546028] "English" "English" "English" "English" ...
 $ marital_status      : chr [1:546028] "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr [1:546028] "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct[1:546028], format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct[1:546028], format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: num [1:546028] 0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, "spec")=
  .. cols(
  ..   subject_id = col_double(),
  ..   hadm_id = col_double(),
  ..   admittime = col_datetime(format = ""),
  ..   dischtime = col_datetime(format = ""),
  ..   deathtime = col_datetime(format = ""),
  ..   admission_type = col_character(),
  ..   admit_provider_id = col_character(),
  ..   admission_location = col_character(),
  ..   discharge_location = col_character(),
  ..   insurance = col_character(),
  ..   language = col_character(),
  ..   marital_status = col_character(),
  ..   race = col_character(),
  ..   edregtime = col_datetime(format = ""),
  ..   edouttime = col_datetime(format = ""),
  ..   hospital_expire_flag = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

str(fread_read)

Classes 'data.table' and 'data.frame':  546028 obs. of  16 variables:
 $ subject_id          : int  10000032 10000032 10000032 10000032 10000068 10000084 10000084 10000108 10000117 10000117 ...
 $ hadm_id             : int  22595853 22841357 25742920 29079034 25022803 23052089 29888819 27250926 22927623 27988844 ...
 $ admittime           : POSIXct, format: "2180-05-06 22:23:00" "2180-06-26 18:27:00" ...
 $ dischtime           : POSIXct, format: "2180-05-07 17:15:00" "2180-06-27 18:49:00" ...
 $ deathtime           : POSIXct, format: NA NA ...
 $ admission_type      : chr  "URGENT" "EW EMER." "EW EMER." "EW EMER." ...
 $ admit_provider_id   : chr  "P49AFC" "P784FA" "P19UTS" "P06OTX" ...
 $ admission_location  : chr  "TRANSFER FROM HOSPITAL" "EMERGENCY ROOM" "EMERGENCY ROOM" "EMERGENCY ROOM" ...
 $ discharge_location  : chr  "HOME" "HOME" "HOSPICE" "HOME" ...
 $ insurance           : chr  "Medicaid" "Medicaid" "Medicaid" "Medicaid" ...
 $ language            : chr  "English" "English" "English" "English" ...
 $ marital_status      : chr  "WIDOWED" "WIDOWED" "WIDOWED" "WIDOWED" ...
 $ race                : chr  "WHITE" "WHITE" "WHITE" "WHITE" ...
 $ edregtime           : POSIXct, format: "2180-05-06 19:17:00" "2180-06-26 15:54:00" ...
 $ edouttime           : POSIXct, format: "2180-05-06 23:30:00" "2180-06-26 21:31:00" ...
 $ hospital_expire_flag: int  0 0 0 0 0 0 0 0 0 0 ...
 - attr(*, ".internal.selfref")=<externalptr> 

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv.)

Solution: The run time is almost the same as me not indicating column data types. And the memory is exactly the same as just using read_csv.

system.time(specified_column<-read_csv("~/mimic/hosp/admissions.csv.gz",
                                       col_types = NULL))

Rows: 546028 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl  (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

   user  system elapsed 
  1.188   0.093   0.661 

object_size(specified_column)

70.02 MB

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 130x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

-rw-r--r--@ 1 yanzisun  staff  2592909134 Oct  3 09:08 /Users/yanzisun/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,2704548,50931,P69FQC,2180-03-23 11:51:00,2180-03-23 15:56:00,___,95,mg/dL,70,100,,ROUTINE,"IF FASTING, 70-100 NORMAL, >125 PROVISIONAL DIABETES."
2,10000032,,36092842,51071,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
3,10000032,,36092842,51074,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
4,10000032,,36092842,51075,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"BENZODIAZEPINE IMMUNOASSAY SCREEN DOES NOT DETECT SOME DRUGS,;INCLUDING LORAZEPAM, CLONAZEPAM, AND FLUNITRAZEPAM."
5,10000032,,36092842,51079,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
6,10000032,,36092842,51087,P69FQC,2180-03-23 11:51:00,,,,,,,,ROUTINE,RANDOM.
7,10000032,,36092842,51089,P69FQC,2180-03-23 11:51:00,2180-03-23 16:15:00,,,,,,,ROUTINE,PRESUMPTIVELY POSITIVE.
8,10000032,,36092842,51090,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,METHADONE ASSAY DETECTS ONLY METHADONE (NOT OTHER OPIATES/OPIOIDS).
9,10000032,,36092842,51092,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"OPIATE IMMUNOASSAY SCREEN DOES NOT DETECT SYNTHETIC OPIOIDS;SUCH AS METHADONE, OXYCODONE, FENTANYL, BUPRENORPHINE, TRAMADOL,;NALOXONE, MEPERIDINE.  SEE ONLINE LAB MANUAL FOR DETAILS."

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 3 minutes on your computer, then abort the program and report your findings.

Solution: My R crashes. The reason is the file is too huge once decompressed. The reading process is taking more memory than my computer’s capability(8gb ram).

read_csv("~/mimic/hosp/labevents.csv.gz")

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

Solution: No, it does not solve the ingestion issue, still taking forever to run chunk. This is probably due to csv files are read row by rows. Selecting columns cannot shorten the time of reading rows.

system.time(read_csv("~/mimic/hosp/labevents.csv.gz", 
                     col_select= c("subject_id", "itemid", 
                                   "charttime", "valuenum")))

Q2.3 Ingest a subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: Use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. Do not put labevents_filtered.csv.gz in Git! To save render time, you can put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true before rendering your qmd file.)

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file, excluding the header? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Solution: There are 32679896 lines in the new filtered file excluding the header. It takes the system around 0.5 seconds for read_csv to ingest the filtered csv.gz.(32 seconds for user)

zcat < ~/mimic/hosp/labevents.csv.gz | awk -F',' '
NR==1 || ($5 == 50912 || $5 == 50971 || $5 == 50983 || $5 == 50902 || 
          $5 == 50882 || $5 == 51221 || $5 == 51301 || $5 == 50931) { 
    print $2 "," $5 "," $7 "," $10 
}' | gzip > labevents_filtered.csv.gz

zcat < labevents_filtered.csv.gz | head -10

subject_id,itemid,charttime,valuenum
10000032,50931,2180-03-23 11:51:00,95
10000032,50882,2180-03-23 11:51:00,27
10000032,50902,2180-03-23 11:51:00,101
10000032,50912,2180-03-23 11:51:00,0.4
10000032,50971,2180-03-23 11:51:00,3.7
10000032,50983,2180-03-23 11:51:00,136
10000032,51221,2180-03-23 11:51:00,45.4
10000032,51301,2180-03-23 11:51:00,3
10000032,51221,2180-05-06 22:25:00,42.6

zcat < labevents_filtered.csv.gz | tail -n +2 |wc -l

zcat: (stdin): unexpected end of file
 5360461

system.time(read.csv("labevents_filtered.csv.gz"))

   user  system elapsed 
  6.134   0.087   6.225 

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory (do not add it in git!). To save render time, put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true when rendering your qmd file.

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is Apache Arrow. Imagine you want to explain it to a layman in an elevator.

Solution: It takes 3.69 seconds of system time for apache arrow to finish the entire process. Apache Arrow is a tool that makes working with big data super fast and simple. Imagine you have a huge file, like a giant table, and instead of making copies or waiting a long time to load it, Arrow lets different programs—like Python, R, or databases—read it directly and quickly. It saves memory and speeds up tasks, like analyzing data or running models. It provides a smart way for different tools to share and work with the same data without wasting time or resources.

system.time({
  apache_tibble <- arrow::open_dataset("labevents.csv", format = "csv") %>%
  filter(itemid %in% c(50912, 50971, 50983, 50902,
                       50882, 51221, 51301, 50931)) %>%
  select(subject_id, itemid, charttime, valuenum) %>%
  arrange(subject_id, charttime, itemid) %>%
  collect()})

   user  system elapsed 
 51.121   3.679  48.765 

head(apache_tibble)

# A tibble: 6 × 4
  subject_id itemid charttime           valuenum
       <int>  <int> <dttm>                 <dbl>
1   10000032  50882 2180-03-23 04:51:00     27  
2   10000032  50902 2180-03-23 04:51:00    101  
3   10000032  50912 2180-03-23 04:51:00      0.4
4   10000032  50931 2180-03-23 04:51:00     95  
5   10000032  50971 2180-03-23 04:51:00      3.7
6   10000032  50983 2180-03-23 04:51:00    136  

nrow(apache_tibble)

[1] 32679896

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is the Parquet format. Imagine you want to explain it to a layman in an elevator.

Solution: The file size is 2.5 GB, and the system time used by the Parquet file is shown below. The row number matches that of Q2.3. Parquet is a way to store data that’s smart and efficient. Instead of saving data row by row like a regular file (like CSV), it saves it column by column. This makes it faster to work with, especially if you only need specific parts of the data. Plus, it compresses the data, so it takes up less space on your computer. Think of it like packing your suitcase neatly to fit more stuff while keeping it easy to find what you need. It’s great for handling big datasets quickly.

system.time({
  arrow::write_dataset(
    open_dataset("labevents.csv", format = "csv"),
    path = "labevents_parquet", 
    format = "parquet"
  )
})

   user  system elapsed 
136.526   7.085  73.986 

system.time({
  parquet_tibble <- arrow::open_dataset("labevents_parquet", 
                                        format = "parquet") %>%
    filter(itemid %in% c(50912, 50971, 50983, 50902, 50882,
               51221, 51301, 50931)) %>%
    select(subject_id, itemid, charttime, valuenum) %>%
    arrange(subject_id, charttime, itemid) %>%
    collect() 
})

   user  system elapsed 
 14.525   3.214   7.251 

head(parquet_tibble, 10)

# A tibble: 10 × 4
   subject_id itemid charttime           valuenum
        <int>  <int> <dttm>                 <dbl>
 1   10000032  50882 2180-03-23 04:51:00     27  
 2   10000032  50902 2180-03-23 04:51:00    101  
 3   10000032  50912 2180-03-23 04:51:00      0.4
 4   10000032  50931 2180-03-23 04:51:00     95  
 5   10000032  50971 2180-03-23 04:51:00      3.7
 6   10000032  50983 2180-03-23 04:51:00    136  
 7   10000032  51221 2180-03-23 04:51:00     45.4
 8   10000032  51301 2180-03-23 04:51:00      3  
 9   10000032  50882 2180-05-06 15:25:00     27  
10   10000032  50902 2180-05-06 15:25:00    105  

nrow(parquet_tibble)

[1] 32679896

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is DuckDB. Imagine you want to explain it to a layman in an elevator.

Solution: It takes around 2 seconds for the system to process the entire workflow. DuckDB is a super-fast, lightweight database designed for data analysis. It’s great for working with large files like CSVs or Parquet, and it can handle millions of rows quickly without using too much memory. DuckDB helps you filter, sort, and analyze data much faster than using traditional data frames. It’s like SQLite for analytics, but designed to crunch numbers at lightning speed.

system.time({
  parquet_dataset <- arrow::open_dataset("labevents_parquet",
                                         format = "parquet")

  duckdb_connection <- dbConnect(duckdb::duckdb())
  duckdb_table <- arrow::to_duckdb(parquet_dataset, con = duckdb_connection)
  
  duckdb_tibble <- duckdb_table %>%
    filter(itemid %in% c(50912, 50971, 50983,
                         50902, 50882, 51221, 51301, 50931)) %>%
    select(subject_id, itemid, charttime, valuenum) %>%
    arrange(subject_id, charttime, itemid) %>%
    collect()  
})

   user  system elapsed 
 20.749   9.884   8.896 

head(duckdb_tibble, 10)

# A tibble: 10 × 4
   subject_id itemid charttime           valuenum
        <dbl>  <dbl> <dttm>                 <dbl>
 1   10000032  50882 2180-03-23 11:51:00     27  
 2   10000032  50902 2180-03-23 11:51:00    101  
 3   10000032  50912 2180-03-23 11:51:00      0.4
 4   10000032  50931 2180-03-23 11:51:00     95  
 5   10000032  50971 2180-03-23 11:51:00      3.7
 6   10000032  50983 2180-03-23 11:51:00    136  
 7   10000032  51221 2180-03-23 11:51:00     45.4
 8   10000032  51301 2180-03-23 11:51:00      3  
 9   10000032  50882 2180-05-06 22:25:00     27  
10   10000032  50902 2180-05-06 22:25:00    105  

nrow(duckdb_tibble)

[1] 32679896

dbDisconnect(duckdb_connection)

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226512,39.4,39.4,kg,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226707,60,60,Inch,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226730,152,152,cm,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,220048,SR (Sinus Rhythm),,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224642,Oral,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224650,None,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:20:00,223761,98.7,98.7,°F,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220179,84,84,mmHg,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220180,48,48,mmHg,0

How many rows? 433 millions.

zcat < ~/mimic/icu/chartevents.csv.gz | tail -n +2 | wc -l

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.

Solution: I choose to use apache arrow with dplyr to answer this question. First, I downloaded the unzipped chartevnts.csv from box folder, then I ingest it with open_dataset function and output a parquet file. Next, I created a tibble collecting the dplyer-filtered dataset. Finally, I showed the nrow and head the first 10 rows. Please remove # since I made a parquet file in my memory and rendering takes too much memory.

#arrow::write_dataset(
    #open_dataset("chartevents.csv", format = "csv"),
    #path = "chartevents_parquet",
    #format = "parquet")

chartevents_tibble <- arrow::open_dataset("chartevents_parquet",
                                          format = "parquet") %>%
    filter(itemid %in% c(220045, 220181, 220179, 223761, 220210)) %>%
  arrange(subject_id, charttime, itemid) %>%
    collect() 

head(chartevents_tibble, 10)

# A tibble: 10 × 11
   subject_id  hadm_id  stay_id caregiver_id charttime          
        <int>    <int>    <int>        <int> <dttm>             
 1   10000032 29079034 39553978        18704 2180-07-23 07:00:00
 2   10000032 29079034 39553978        18704 2180-07-23 07:11:00
 3   10000032 29079034 39553978        18704 2180-07-23 07:11:00
 4   10000032 29079034 39553978        18704 2180-07-23 07:12:00
 5   10000032 29079034 39553978        18704 2180-07-23 07:12:00
 6   10000032 29079034 39553978        18704 2180-07-23 07:30:00
 7   10000032 29079034 39553978        18704 2180-07-23 07:30:00
 8   10000032 29079034 39553978        18704 2180-07-23 07:30:00
 9   10000032 29079034 39553978        18704 2180-07-23 07:30:00
10   10000032 29079034 39553978        18704 2180-07-23 08:00:00
# ℹ 6 more variables: storetime <dttm>, itemid <int>, value <chr>,
#   valuenum <dbl>, valueuom <chr>, warning <int>

nrow(chartevents_tibble)

[1] 30195426